Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification

Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then the fused image is classified into change and unchanged regions. The presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification errors. The proposed method was compared with six state-of-theart change detection methods and analyzed. The main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels, the mixed pixel problem is solved. Experiments indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors, higher overall accuracy and kappa coefficient.     

基于多层次视觉语义特征融合的图像检索算法

目的为了解决低层特征与中层语义属性间出现的语义鸿沟,以及在将低层特征转化为语义属性的过程中易丢失信息,从而会降低检索精度等问题,设计一种多层次视觉语义特征融合的图像检索算法。方法首先分别提取图像的3种中层特征(深度卷积神经网络(DCNN)特征、Fisher向量、稀疏编码空间金字塔匹配特征(SCSPM));其次,为了对3种特征进行有效融合,定义一种基于图的半监督学习模型,将提取的3个中层特征进行融合,形成一个多层次视觉语义特征,有效结合3种不同中层特征的互补信息,提高图像特征描述,从而降低检索算法中的语义鸿沟;最后,引入具有视觉特性与语义统一的距离函数,根据提取的多层次视觉语义特征来计算查询图像和训练图像的相似度量,完成图像检索任务。结果实验结果表明,与当前检索方法对比,文中算法具有更高的检索精度与效率。结论所提算法具有良好的检索准确度,在医疗、包装商标等领域具有一定的参考价值。     

基于YOLOv3检测和特征点匹配的多目标跟踪算法

针对传统多目标跟踪算法中行人检测速度慢、易受光照变化、行人快速移动及部分遮挡因素的影响造成行人目标跟踪性能差等问题,提出一种根据经典的Tracking-by-Detection模式,采用深度学习YOLOv3算法检测行人目标,然后利用FAST角点检测算法与BRISK特征点描述算法对相邻帧间的行人目标进行特征点匹配,实现多...     

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this study, an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning (AATC-HTHDL) model is proposed. The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text. The first step in the proposed model is to pre-process the input data to transform it into a useful format. The Term Frequency-Inverse Document Frequency (TF-IDF) model is applied to extract the feature vectors. Next, the Convolutional Neural Network with Recurrent Neural Network (CRNN) model is utilized to classify the Arabic text. In the final stage, the Crow Search Algorithm (CSA) is applied to fine-tune the CRNN model’s hyperparameters, showing the work’s novelty. The proposed AATC-HTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.     

基于深度学习耦合稀疏语义度量的商标检索算法

目的针对当前商标图像检索中的语义鸿沟问题,提出一种深度学习耦合稀疏语义度量的商标图像检索方案,有效抑制噪声干扰,降低冗余特征维数。方法首先,根据由卷积与池化组成的无监督学习机制,对输入商标图像进行多层特征提取,输出一维特征向量。随后,通过L2-支持向量机(L2-SVM)进行分类,利用特征向量进行训练,获得多级联特征。然后,根据商标图像的多级联特征和用户标签信息的异构数据结构,设计一种稀疏语义度量方法进行相似检索,减少语义鸿沟。此外,引入一种混合范数作为相似度量的稀疏约束,以抑制原始输入空间中的冗余特征维数和噪声,优化检索结果。结果实验表明,与当前流行的商标检索方案相比,所提算法具有更高的检索精度,其输出的结果中仅有1幅无关图像。结论该方案具有较高的检索精度和较强的鲁棒性,在商标检测、商标保护等方面中具有良好的应用价值。     

Joint Deep Matching Model of OCT Retinal Layer Segmentation

Optical Coherence Tomography (OCT) is very important in medicine and provide useful diagnostic information. Measuring retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing retinal layer segmentation approaches, learning or deep learning-based methods belong to the state-of-art. However, most of these techniques rely on manual-marked layers and the performances are limited due to the image quality. In order to overcome this limitation, we build a framework based on gray value curve matching, which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from OCT. The depth convolution network learns the column correspondence in the OCT image unsupervised. The whole OCT image participates in the depth convolution neural network operation, compares the gray value of each column, and matches the gray value sequence of the transformation column and the next column. Using this algorithm, when a boundary point is manually specified, we can accurately segment the boundary between retinal layers. Our experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.     

Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer in the training process of the main task-semantic role labeling, so the argument-related structural information selected by the predicate through the attention mechanism is used to assist the main task. Because the model makes full use of the semantic dependency between part-of-speech and semantic roles and the structural information of predicateargument, our model achieved the F1 value of 89.0% on the WSJ test set of CoNLL2005, which is superior to existing state-of-the-art model about 0.8%.     

Natural Language Semantic Construction Based on Cloud Database

Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine. It is the basis for realizing the information exchange in the intelligent cloud-computing environment. This paper proposes a natural language semantic construction method based on cloud database, mainly including two parts: natural language cloud database construction and natural language semantic construction. Natural Language cloud database is established on the CloudStack cloud-computing environment, which is composed by corpus, thesaurus, word vector library and ontology knowledge base. In this section, we concentrate on the pretreatment of corpus and the presentation of background knowledge ontology, and then put forward a TF-IDF and word vector distance based algorithm for duplicated webpages (TWDW). It raises the recognition efficiency of repeated web pages. The part of natural language semantic construction mainly introduces the dynamic process of semantic construction and proposes a mapping algorithm based on semantic similarity (MBSS), which is a bridge between Predicate-Argument (PA) structure and background knowledge ontology. Experiments show that compared with the relevant algorithms, the precision and recall of both algorithms we propose have been significantly improved. The work in this paper improves the understanding of natural language semantics, and provides effective data support for the natural language interaction function of the cloud service.     

Optimized Deep Learning Model for Fire Semantic Segmentation

Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset of low-level features. Besides, an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results. We compare the proposed method with existing advanced semantic segmentation and salient object detection methods. Experimental results demonstrate that the proposed method achieves 94.1%, 93.6%, 94.6%, 95.3%, and 95.9% Intersection over Union (IoU) on five test sets respectively which outperforms the suboptimal method by a large margin. In addition, in terms of accuracy, our approach also achieves the best score.     

基于深度学习利用特征图加权融合的目标检测方法

为了提高目标检测的准确性,提出了一种基于深度学习利用特征图加权融合实现目标检测的方法。首先,提出将卷积神经网络中的浅层特征图采样后与最深层特征图进行加权融合的思想;其次,根据所提的特征图加权融合思想以及卷积神经网络的具体结构,制定相应的特征图加权融合方案,并由该方案得到新特征图;然后,提出改进的RPN网络,并将新特征图输入到改进的RPN网络得到区域建议;最后,将新特征图和区域建议输入到后续网络层完成目标检测。实验结果表明所提方法取得了更高的目标检测精度以及更好的目标检测效果。     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to an optimum selection of feature subsets. Moreover, Deep Random Vector Functional Link network (DRVFLN) classification technique was applied to properly allot the class labels to the financial data. Furthermore, improved fruit fly optimization algorithm (IFFOA) based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model. For enhancing the better performance of the IFSDL-FRA technique, an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.     

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

基于语义量化匹配的汽车外观显著性意象挖掘

目的 为解决传统感性设计研究中意象实验耗时大以及小样本偶然性等问题,依托现有网络评价文本信息提取了用户意象认知。方法 首先,爬取大规模汽车外观评论文本,构建语义分析词汇库,构建word2vec词向量模型;然后,基于模型获取词库内部的语义联系,计算高频关键形容词之间的语义离散性,以构建代表性意象词空间;最后,通过语义量化匹配将评论映射到意象词空间,得到大规模用户对各车型的显著性意象表征,明确了指定意象词汇下的汽车外观匹配结果。结果 运用该方法挖掘汽车外观显著性意象与基于人工评价的实验结果无显著性差异且具有高度相关性,证明了该方法的有效性。结论 以该方法挖掘用户意象认知,运用了现有的大批量用户反馈知识,提高了意象分析效率,有助于决策者快速理解消费者对汽车外观的感性知识,在设计迭代中可使产品更符合市场期望;对比相关研究,基于语义量化匹配的方式无需对超高维向量进行降维和聚类,避免了以往研究因特征降维而可能导致的词向量语义联系的损失,以得到更为准确的意象挖掘结果。     

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced. Further, a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions. Novelty of DSCNet model lies in introducing the deep contextual. DSCNet is evaluated on three datasets i.e., Restaurant, Laptop, and Twitter dataset considering different deep learning (DL) metrics like precision, recall, accuracy, and Macro-F1 score. Also, comparative analysis is carried out with different baseline methods in terms of accuracy and Macro-F1 score. DSCNet achieves 92.59% of accuracy on restaurant dataset, 86.99% of accuracy on laptop dataset and 78.76% of accuracy on Twitter dataset.     

基于能力需求匹配模型的老年图标设计

目的 现有图标语义与设计元素之间通常缺乏明确的对应关系,导致老年用户在辨识图标语义时常因心智能力不足而遇到困难,需要平衡老年用户图标辨识能力与产品施加给用户的挑战之间的关系,并搭建设计师与用户之间的沟通桥梁。方法 前期以心流理论和能力需求匹配梯度模型为基础,将家电图标按照认知方式进行分类收集。后期进行老年语义认知能力评估测试,具体阐述用户能力与图标认知效率的联系。结果 建立老年认知能力需求匹配梯度,通过洗衣机“手洗模式”图标设计,对该梯度的有效性进行验证。结论 在设计多样性的要求下,在图标设计时可优先选择能力余裕范围,适度选择能力适配范围,避免选择用户能力不足范围,从而为老年人提供更多元的设计选择。     

基于深度学习的易拉罐缺陷检测技术

目的 现有的易拉罐缺陷检测系统在高速生产线中存在错检率和漏检率高,检测精度相对较低等问题,为了提高易拉罐缺陷识别的准确性,使易拉罐生产线实现进一步自动化、智能化,基于深度学习技术和迁移学习技术,提出一种适用于易拉罐制造的在线检测的算法。方法 利用深度卷积网络提取易拉罐缺陷特征,通过优化卷积核,减短易拉罐缺陷检测的时间。针对国内外数据集缺乏食品包装制造的缺陷图像,构建易拉罐缺陷数据集,结合预训练网络,通过调整VGG16提升对易拉罐缺陷的识别准确率。结果 对易拉罐数据集在卷积神经网络、迁移学习和调整后的预训练网络进行了易拉罐缺陷检测的性能对比,验证了基于深度学习的易拉罐缺陷检测技术在学习率为0.0005,训练10个迭代后可达到较好的识别效果,最终二分类缺陷识别率为99.7%,算法耗时119 ms。结论 相较于现有的易拉罐检测算法,文中提出的基于深度学习的易拉罐检测算法的识别性能更优,智能化程度更高。同时,该研究有助于制罐企业利用深度学习等AI技术促进智能化生产,减少人力成本,符合国家制造业产业升级的策略,具有一定的实际意义。     

基于均衡化概率模型的特征匹配及其应用

通过对匹配模型中邻接矩阵的均衡化分析,在概率框架下提出一种新的特征匹配算法.采用重启动的随机游走方法建立并求解概率模型,并对匹配邻接矩阵进行了均衡化分析,提出了一种有效的双向均衡方法.方法不仅考虑了两个待匹配特征点的全部几何关联以及各项关联之间的权重值,而且考虑了关联权重的均衡性,从而可加强匹配的区分度,提高匹配的准确...     

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.